POS0160 ARTIFICIAL INTELLIGENCE ALGORITHMS FOR AUTOMATIC STRATIFICATION OF RHEUMATOID ARTHRITIS USING HAND X-RAYS
Authors: M. Bonnin, T. Estienne, F. Muller-Fouarge, S. Bekadar, Y. Carrillon, C. Pouchy, T. Ait Si Selmi
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> The diagnosis of rheumatoid arthritis is based on clinical, biological and imaging criteria [1]. Among these criteria, the quantification of the lesions through X-ray images is essential for the establishment of therapeutic strategies and the clinical follow-up of the patient [2]. The Sharp/van der Heijde (SvH) method is used to assess the severity of bone erosion and joint narrowing on X-rays of hands and feet: it is frequently used in clinical practices and observational studies [3]. The SvH manual scoring process requires a high level of expertise and is time consuming. It can also suffer from inter-observer interpretation variability. <h3>Objectives</h3> The objective of this study is to use convolutional neural networks to automate the scoring of SvH and the classification of patients on this scale. <h3>Methods</h3> This work uses a public database of 3,818 X-rays of both hands. Each X-ray is paired with a rating of the disease' severity evaluated with the SvH score. This score has values between 0 and 280 because only both hands are used. The database was separated into a training, validation and testing set (64%, 16% and 20% respectively). Two convolutional neural networks (CNN) were trained. The first predicts the value of the SvH score. The second classifies patients into two groups, depending on their score: less or more than 70. To improve the performance of the algorithms, various techniques have been implemented: transfer learning, data augmentation functions (rotation, flipping, noise) and hyperparameter and architecture search. <h3>Results</h3> The prediction network has an average error of 16.00 (std 17.75), with a Pearson correlation of 0.86. Its results are presented in the figure with individual results and confidence intervals. The classification algorithm obtained a mean value of the area under the curve (AUC) of 0.97, an accuracy of 0.84 and a positive predictive value of 0.91. <h3>Conclusion</h3> This study demonstrates the interest of using artificial intelligence algorithms in the diagnosis and standardized follow-up of patients with rheumatoid arthritis. These could help clinicians in the evaluation of osteoarticular lesions and the stratification of patients according to the severity of the lesions with great precision. <h3>References</h3> [1]Funovits J, Aletaha D, Bykerk V, Combe B, Dougados M, Emery P et <i>al.</i> Rheumatism classification criteria for rheumatoid arthritis: methodological report Phase 1. Ann Rheum Dis 2010; 69: 1589–95. [2]Colebatch AN, Edwards CJ, Ostergaard M,. EULAR recommendations for the use of imaging of the joints in the clinical management of rheumatoid arthritis. Ann Rheum Dis 2013;72:804-14. [3]Van der Heijde, D. M. F. M. (1999). How to read radiographs according to the Sharp/van der Heijde method. <i>The Journal of rheumatology</i>, <i>26</i>(3), 743-745. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.